An MRI Brain Image Segmentation and Tumor
Detection using SOM-Clustering and PSVM
Classifier
Journal:
GRENZE International Journal of Engineering and Technology
Authors:
D. Rammurthy, Mahesh P. K
Volume:
3
Issue:
3
Grenze ID:
01.GIJET.3.3.3
Pages:
1-9
Abstract
In recent days, image processing is widely used in diagnosis of disease such as
brain tumor, Cancer, Diabetes etc. Brain tumor is one such dangerous disease and currently
moreover 600,000 people have this type of disease. Image segmentation is an important
technique highly used to extract the suspicious parts from medical images such as MRI, CT
scan, and Mammography etc. With this motivation in this work, SOM clustering is
proposed for MRI brain image segmentation. Before the segmentation the Histogram
Equalization is utilized for feature extraction which will improve the segmentation
accuracy. After the segmentation process, the feature extraction using Gray Level Cooccurrence
Matrix is utilized which avoids the formation of misclustered regions. The
Principle Component Analysis (PCA) method is used for the feature selection to improve the
classifier accuracy. An effective classifier Proximal Support Vector Machines (PSVM) is
used to automatically detect the tumor from MRI brain image. This method is faster and
computationally more efficient than the existing method SVM.